randomgen 1.26.1

Description:

randomgen 1.26.1

RandomGen
This package contains additional bit generators for NumPy's
Generator and an ExtendedGenerator exposing methods not in Generator.
Continuous Integration


Coverage

Latest Release


License



This is a library and generic interface for alternative random
generators in Python and NumPy.
New Features
The the development documentation for the latest features,
or the stable documentation for the latest released features.
WARNINGS
Changes in v1.24
Generator and RandomState were removed in 1.23.0.
Changes from 1.18 to 1.19
Generator and RandomState have been officially deprecated in 1.19, and will
warn with a FutureWarning about their removal. They will also receive virtually
no maintenance. It is now time to move to NumPy's np.random.Generator which has
features not in randomstate.Generator and is maintained more actively.
A few distributions that are not present in np.random.Generator have been moved
to randomstate.ExtendedGenerator:

multivariate_normal: which supports broadcasting
uintegers: fast 32 and 64-bit uniform integers
complex_normal: scalar complex normals

There are no plans to remove any of the bit generators, e.g., AESCounter,
ThreeFry, or PCG64.
Changes from 1.16 to 1.18
There are many changes between v1.16.x and v1.18.x. These reflect API
decision taken in conjunction with NumPy in preparation of the core
of randomgen being used as the preferred random number generator in
NumPy. These all issue DeprecationWarnings except for BasicRNG.generator
which raises NotImplementedError. The C-API has also changed to reflect
the preferred naming the underlying Pseudo-RNGs, which are now known as
bit generators (or BigGenerators).
Future Plans

Add some distributions that are not supported in NumPy. Ongoing
Add any interesting bit generators I come across. Recent additions include the DXSM and CM-DXSM variants of PCG64 and the LXM generator.

Included Pseudo Random Number Generators
This module includes a number of alternative random
number generators in addition to the MT19937 that is included in NumPy.
The RNGs include:

Cryptographic cipher-based random number generator based on AES, ChaCha20, HC128 and Speck128.
MT19937,
the NumPy rng
dSFMT a
SSE2-aware version of the MT19937 generator that is especially fast at
generating doubles
xoroshiro128+,
xorshift1024*φ,
xoshiro256**,
and xoshiro512**
PCG64
ThreeFry and Philox from Random123
Other cryptographic-based generators: AESCounter, SPECK128, ChaCha, and HC128.
Hardware (non-reproducible) random number generator on AMD64 using RDRAND.
Chaotic PRNGS: Small-Fast Chaotic (SFC64) and Jenkin's Small-Fast (JSF).

Status

Builds and passes all tests on:

Linux 32/64 bit, Python 3.7, 3.8, 3.9, 3.10
Linux (ARM/ARM64), Python 3.8
OSX 64-bit, Python 3.9
Windows 32/64 bit, Python 3.7, 3.8, 3.9, 3.10
FreeBSD 64-bit



Version
The package version matches the latest version of NumPy when the package
is released.
Documentation
Documentation for the latest release is available on
my GitHub pages. Documentation for
the latest commit (unreleased) is available under
devel.
Requirements
Building requires:

Python (3.6, 3.7, 3.8, 3.9, 3.10)
NumPy (1.17+)
Cython (0.29+)
tempita (0.5+), if not provided by Cython

Testing requires pytest (6+).
Note: it might work with other versions but only tested with these
versions.
Development and Testing
All development has been on 64-bit Linux, and it is regularly tested on
Azure (Linux-AMD64, Window, and OSX) and Cirrus (FreeBSD and Linux-ARM).
Tests are in place for all RNGs. The MT19937 is tested against
NumPy's implementation for identical results. It also passes NumPy's
test suite where still relevant.
Installing
Either install from PyPi using
python -m pip install randomgen

or, if you want the latest version,
python -m pip install git+https://github.com/bashtage/randomgen.git

or from a cloned repo,
python -m pip install .

If you use conda, you can install using conda forge
conda install -c conda-forge randomgen

SSE2
dSFTM makes use of SSE2 by default. If you have a very old computer
or are building on non-x86, you can install using:
export RANDOMGEN_NO_SSE2=1
python -m pip install .

Windows
Either use a binary installer, or if building from scratch, use
Python 3.6/3.7 with Visual Studio 2015 Build Toolx.
License
Dual: BSD 3-Clause and NCSA, plus sub licenses for components.